English(EN) Stealthy in Semantics, Antagonistic in Space: Attacking Visible-Infrared Object Detectors via Object-Level Misalignment

新的CamoShift框架攻击可见-红外目标检测器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了CamoShift，一个旨在攻击可见-红外目标检测器的新型对抗性框架。该方法结合了视觉伪装和对象级红外偏移，以破坏跨模态空间对齐和融合过程。CamoShift旨在实现攻击有效性和视觉隐蔽性之间的卓越平衡，性能优于现有的物理攻击方法。 AI

影响引入了可见-红外目标检测的新型对抗技术，可能影响鲁棒性测试和安全性。

排序理由该项目是一篇学术论文，详细介绍了一种用于计算机视觉研究的新型对抗性框架。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Yueqi Zhu, Qi Ming, Guo Cheng, Yongkang Zhang, Feiran Liu, Juan Fang, Jiahuan Zhou, Jiangmeng Li, Yuhan Zhang · 2026-09-17 04:00

语义隐匿，空间对抗：通过对象级错位攻击可见光-红外目标检测器

arXiv:2609.18133v1 Announce Type: new Abstract: Visible-infrared object detectors are used for robust perception under challenging illumination and weather conditions. Current physical attacks apply conspicuous patches to spatially aligned target regions, which are noticeable to …